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| Section | Objectives |
|---|---|
| Topic 1: Develop AI solutions by using Azure data management services | - Work with Azure data platforms for AI workloads
|
| Topic 2: Develop containerized solutions on Azure | - Implement containerized applications
|
| Topic 3: Connect to and consume Azure services | - Integrate Azure services
|
| Topic 4: Secure, monitor, troubleshoot Azure solutions | - Operate AI cloud solutions
|
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NEW QUESTION # 93
An ACA app processes messages from an Azure Storage queue.
The app must scale automatically based on messages in a specific Azure Storage queue by using a Kubernetes Event-driven Autoscaler (KEDA) custom scale rule.
You need to configure the required scale rule values.
Which two values should you configure? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: B,D
Explanation:
[A]
QeueName: The exact name of the specific Azure Storage queue your app is polling is required.
[C]
Why Trigger Type is Required
When configuring a custom scale rule in Azure Container Apps (ACA), the platform utilizes KEDA underneath. Because a custom rule can connect to many different event sources (such as Kafka, Redis, or Azure Storage), you must explicitly define the Trigger type (e.g., azure-queue) so KEDA knows which specific scaler to initiate.
Reference:
https://techcommunity.microsoft.com/blog/fasttrackforazureblog/container-apps-a-practical-scaling-with-azure-queue-scale-rule/3722075
NEW QUESTION # 94
A development team wants to compare multiple prompt variations against the same test dataset and visualize which prompt performs best. What should they use in Azure AI Foundry?
Answer: D
Explanation:
Prompt flow variants allow you to define multiple versions of a prompt/node, run them against the same evaluation dataset, and compare results side-by-side to identify the best-performing variant.
NEW QUESTION # 95
You are developing a Java application to be deployed in Azure. The application stores sensitive data in Azure Cosmos DB.
You need to configure Always Encrypted to encrypt the sensitive data inside the application.
What should you do first?
Answer: B
Explanation:
Detailed Explanation: For Cosmos DB Always Encrypted, the key hierarchy starts with a customer-managed key held in Azure Key Vault. The data encryption key used by the client is protected by that customer- managed key. Microsoft's setup sequence therefore begins by preparing Key Vault and the CMK before creating DEKs or an encryption policy. Creating a managed identity, a container policy, or a DEK can be necessary later, but none precedes establishing the CMK that protects the client-side encryption key material.
Study Guide Alignment: AI data-management workloads: Cosmos DB, PostgreSQL, caching, vector storage, vector retrieval, consistency, and connection optimization.
Official Microsoft Learn References: AI-200 Study Guide | Always Encrypted for Azure Cosmos DB
NEW QUESTION # 96
You deploy an AI application across multiple Azure regions.
The application must be able to view writes across all regions within a predictable time window.
You need to determine the appropriate consistency level.
What are two consistency levels you can use to achieve the goal? Each correct answer presents a complete solution. Choose two.
NOTE: Each correct selection is worth one point.
Answer: A,C
NEW QUESTION # 97
A container in an AKS cluster repeatedly restarts.
Pod events show probe failures, although node-level CPU and memory metrics are normal.
You need to diagnose the cause of the repeating restarts.
What should you do first?
Answer: A
Explanation:
The first troubleshooting action should be to inspect the pod events and container logs . Microsoft's AKS troubleshooting guidance identifies Kubernetes events and application logs as primary diagnostic sources when a pod repeatedly restarts or enters states such as CrashLoopBackOff. kubectl describe pod < pod-name
> exposes events including failed liveness or readiness probes, restart reasons, scheduling problems, and container termination information. kubectl logs < pod-name > provides application-level output, while kubectl logs < pod-name > --previous is particularly useful when the container has already restarted because it retrieves logs from the previous terminated container instance.
The scenario already states that node CPU and memory are normal, making node resource pressure less likely.
Probe failures can instead result from incorrect probe paths, ports, timeouts, dependency checks, slow application initialization, or application errors. Microsoft specifically recommends reviewing health probe configuration and application behavior before modifying infrastructure.
Scaling replicas does not identify the root cause. Decreasing initialDelaySeconds can actually make a slow- starting application fail sooner. Draining or rebooting the node is unjustified without evidence of a node-level fault.
Study Guide references: AKS troubleshooting # Kubernetes events, container logs, liveness/readiness probes, kubectl describe, kubectl logs --previous.
NEW QUESTION # 98
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